Dataset Information

Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer.

ABSTRACT:

Background

Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome.

Methods

Forty-three patients under treatment in the IRCCS Sant'Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score.

Results

The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results.

Conclusions

The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice.

SUBMITTER: Filitto G

PROVIDER: S-EPMC9100060 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer.

Filitto Giuseppe G Coppola Francesca F Curti Nico N Giampieri Enrico E Dall'Olio Daniele D Merlotti Alessandra A Cattabriga Arrigo A Cocozza Maria Adriana MA Taninokuchi Tomassoni Makoto M Remondini Daniel D Pierotti Luisa L Strigari Lidia L Cuicchi Dajana D Guido Alessandra A Rihawi Karim K D'Errico Antonietta A Di Fabio Francesca F Poggioli Gilberto G Morganti Alessio Giuseppe AG Ricciardiello Luigi L Golfieri Rita R Castellani Gastone G

Cancers 20220429 9

<h4>Background</h4>Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using ...[more]

PMID: 35565360

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Project description:BackgroundNeoadjuvant chemoradiotherapy (nCRT) prior to surgery is the standard treatment for patients with locally advanced rectal cancer (LARC), while parts of them show poor therapeutic response accompanied by therapy adverse effects. Predictive biomarkers for nCRT response could facilitate the guidance on treatment decisions but are still insufficient until now, which limits the clinical applications of nCRT in LARC patients.MethodsIn our study, 37 formalin-fixed paraffin-embedded tumor biopsies were obtained from patients with LARC before receiving 5-fluorouracil based nCRT. Proteomics analyses were conducted to identify the differentially expressed proteins (DEPs) between total responders (TR) and poor responders (PR). The DEPs were validated via ROC plotter web tool and their predictive performance was evaluated by receiver operating characteristic analysis. Functional enrichment analyses were performed to further explore the potential mechanisms underlying nCRT response.ResultsAmong 3,998 total proteins, 91 DEPs between TR and PR were screened out. HSPA4, NIPSNAP1, and SPTB all with areas under the curve (AUC) ~ 0.8 in the internal discovery cohort were independently validated by the external mRNA datasets (AUC ~ 0.7), and their protein levels were linearly correlated with the graded responses to nCRT in the internal cohort. The combination of HSPA4 and SPTB could distinctly discriminate the TR and PR groups (AUC = 0.980, p < 0.0001). Moreover, multiple combinations of the three proteins realized increased specificity and/or sensitivity, while achieving favorable predictive value when moderate responders were introduced into the ROC analysis. Pathways including DNA damage repair, cell cycle, and epithelial mesenchymal transition were involved in nCRT response according to the enrichment analysis results.ConclusionsHSPA4, SPTB and NIPSNAP1 in tumor biopsies and/or their optional combinations might be potential predictive markers for nCRT response in patients with LARC. The DEPs and their related functions have implications for the potential mechanisms of treatment response to nCRT in patients with LARC.

Project description:Objective: Our objective was to develop a radiomics model based on magnetic resonance imaging (MRI) and contrast-enhanced computed tomography (CE-CT) to predict pathological complete response (pCR) to neoadjuvant treatment in locally advanced rectal cancer (LARC). Material: All patients treated for a LARC with neoadjuvant CRT and subsequent surgery in two separate institutions between 2012 and 2019 were considered. Both pre-CRT pelvic MRI and CE-CT were mandatory for inclusion. The tumor was manually segmented on the T2-weighted and diffusion axial MRI sequences and on CE-CT. In total, 88 radiomic parameters were extracted from each sequence using the Miras© software, with a total of 822 features by patient. The cohort was split into training (Institution 1) and testing (Institution 2) sets. The ComBat and Synthetic Minority Over-sampling Technique (SMOTE) approaches were used to account for inter-institution heterogeneity and imbalanced data, respectively. We selected the most predictive characteristics using Spearman's rank correlation and the Area Under the ROC Curve (AUC). Five pCR prediction models (clinical, radiomics before and after ComBat, and combined before and after ComBat) were then developed on the training set with a neural network approach and a bootstrap internal validation (n = 1000 replications). A cut-off maximizing the model's performance was defined on the training set. Each model was then evaluated on the testing set using sensitivity, specificity, balanced accuracy (Bacc) with the predefined cut-off. Results: Out of the 124 included patients, 14 had pCR (11.3%). After ComBat harmonization, the radiomic and the combined models obtained a Bacc of 68.2% and 85.5%, respectively, while the clinical model and the pre-ComBat combined achieved respective Baccs of 60.0% and 75.5%. Conclusions: After correction of inter-site variability and imbalanced data, addition of radiomic features enhances the prediction of pCR after neoadjuvant CRT in LARC.

Project description:BackgroundThis study aimed to develop a model that automatically predicts the neoadjuvant chemoradiotherapy (nCRT) response for patients with locally advanced cervical cancer (LACC) based on T2-weighted MR images and clinical parameters.MethodsA total of 138 patients were enrolled, and T2-weighted MR images and clinical information of the patients before treatment were collected. Clinical information included age, stage, pathological type, squamous cell carcinoma (SCC) level, and lymph node status. A hybrid model extracted the domain-specific features from the computational radiomics system, the abstract features from the deep learning network, and the clinical parameters. Then, it employed an ensemble learning classifier weighted by logistic regression (LR) classifier, support vector machine (SVM) classifier, K-Nearest Neighbor (KNN) classifier, and Bayesian classifier to predict the pathologic complete response (pCR). The area under the receiver operating characteristics curve (AUC), accuracy (ACC), true positive rate (TPR), true negative rate (TNR), and precision were used as evaluation metrics.ResultsAmong the 138 LACC patients, 74 were in the pCR group, and 64 were in the non-pCR group. There was no significant difference between the two cohorts in terms of tumor diameter (p = 0.787), lymph node (p = 0.068), and stage before radiotherapy (p = 0.846), respectively. The 109-dimension domain features and 1472-dimension abstract features from MRI images were used to form a hybrid model. The average AUC, ACC, TPR, TNR, and precision of the proposed hybrid model were about 0.80, 0.71, 0.75, 0.66, and 0.71, while the AUC values of using clinical parameters, domain-specific features, and abstract features alone were 0.61, 0.67 and 0.76, respectively. The AUC value of the model without an ensemble learning classifier was 0.76.ConclusionsThe proposed hybrid model can predict the radiotherapy response of patients with LACC, which might help radiation oncologists create personalized treatment plans for patients.

Dataset Information

Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer.

Background

Methods

Results

Conclusions

Publications

Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer.

Similar Datasets

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